• Journal of the Korean Geographical Society
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• v.51 no.6
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• pp.779-797
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• 2016

A calibrated hydrological model is a useful tool for quantifying the impacts of the climate variations and land use/land cover changes on sediment load, water quality and runoff. In the rainy season each year, the Xe Bang Fai river basin is provisionally flooded because of typhoons, the frequency and intensity of which are sensitive to ongoing climate change. Severe heavy rainfall has continuously occurred in this basin area, often causing severe floods at downstream of the Xe Bang Fai river basin. The main purpose of this study is to investigate the climate change impact on river discharge using a Soil and Water Assessment Tool (SWAT) model based on future climate change scenarios. In this study, the simulation of hydrological river discharge is used by SWAT model, covering a total area of $10,064km^2$ in the central part of country. The hydrological model (baseline) is calibrated and validated for two periods: 2001-2005 and 2006-2010, respectively. The monthly simulation outcomes during the calibration and validation model are good results with $R^2$ > 0.9 and ENS > 0.9. Because of ongoing climate change, three climate models (IPSL CM5A-MR 2030, GISS E2-R-CC 2030 and GFDL CM3 2030) indicate that the rainfall in this area is likely to increase up to 10% during the summer monsoon season in the near future, year 2030. As a result of these precipitation increases, the SWAT model predicts rainy season (Jul-Aug-Sep) river discharge at the Xebangfai@bridge station will be about $800m^3/s$ larger than the present. This calibrated model is expected to contribute for preventing flood disaster risk and sustainable development of Laos

• Journal of Wetlands Research
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• v.25 no.2
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• pp.75-82
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• 2023

Data for measuring the flow rate of streams are used as important basic data for the development and maintenance of water resources, and many experts are conducting research to make more accurate measurements. Especially, in Korea, monsoon rains and heavy rains are concentrated in summer due to the nature of the climate, so floods occur frequently. Therefore, it is necessary to measure the flow rate most accurately during a flood to predict and prevent flooding. Thus, the U.S. Geological Survey (USGS) introduces 1, 2, 3 point method using a flow meter as one way to measure the average flow rate. However, it is difficult to calculate the average flow rate with the existing 1, 2, 3 point method alone.This paper proposes a new 1, 2, 3 point method formula, which is more accurate, utilizing one probabilistic entropy concept. This is considered to be a highly empirical study that can supplement the limitations of existing measurement methods. Data and Flume data were used in the number of holesman to demonstrate the utility of the proposed formula. As a result of the analysis, in the case of Flume Data, the existing USGS 1 point method compared to the measured value was 7.6% on average, 8.6% on the 2 point method, and 8.1% on the 3 point method. In the case of Coleman Data, the 1 point method showed an average error rate of 5%, the 2 point method 5.6% and the 3 point method 5.3%. On the other hand, the proposed formula using the concept of entropy reduced the error rate by about 60% compared to the existing method, with the Flume Data averaging 4.7% for the 1 point method, 5.7% for the 2 point method, and 5.2% for the 3 point method. In addition, Coleman Data showed an average error of 2.5% in the 1 point method, 3.1% in the 2 point method, and 2.8% in the 3 point method, reducing the error rate by about 50% compared to the existing method.This study can calculate the average flow rate more accurately than the existing 1, 2, 3 point method, which can be useful in many ways, including future river disaster management, design and administration.